Distributional effects of transport policies on inequalities in access to opportunities in Rio de Janeiro
Bibliographic record
Abstract
The evaluation of social impacts of transport policies has been attracting growing attention in recent years. Yet studies thus far have predominately focused on developed countries and overlooked whether equity assessment of transport projects is sensitive to the modifiable areal unit problem (MAUP). This paper investigates how investments in public transport can reshape socio-spatial inequalities in access to opportunities, and it examines how MAUP can influence the distributional effects of transport project evaluations. The study looks at Rio de Janeiro (Brazil) and the transformations carried out in the city in preparation for the 2014 World Cup and the 2016 Olympics, which involved substantial expansion in public transport infrastructure followed by cuts in service levels. The paper uses before-and-after comparison of Rio's transport network (2014-2017) and quasi-counterfactual analysis to examine how those policies affect access to schools and jobs for different income groups and whether the results are robust when the data is analyzed at different spatial scales and zoning schemes. Results show that subsequent cuts in service levels have offset the accessibility benefits of transport investments in a way that particularly penalizes the poor, and that those investments alone would still have generated larger accessibility gains for higher-income groups. These findings suggest that, contrary to Brazil’s official discourse of transport legacy, recent policies in Rio have exacerbated rather than reduced socio-spatial inequalities in access to opportunities. The study also shows that MAUP can influence the equity assessment of transport projects, suggesting that this issue should be addressed in future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".